Chapter 1
Introduction
Decision Support Systems
For Business Intelligence
DSS in Action
Equifax provides DSS and supporting databases to many of America’s Fortune 1000 companies which allow these
businesses to make more effective and profitable business decisions. The system allows users access to more than 60
national databases, mapping software, and analysis tools so that users can define and analyze its opportunities in a
geographic area.
The tool enables retailers, banks and other businesses to display trade areas and then to analyze demographic attributes.
In particular, this DSS integrates customer information with current demographic and locational data. For example,
Consumer-Facts (tm), offers information about spending patterns of more than 400 products and services in more than 15
major categories, with regional spending patterns incorporated. Further, it provides five-year projections that reflect the
impact of dynamic economic and demographic conditions, such as income, employment, population and household
changes, on consumer spending that can be integrated with a corporation’s own customer information.
This coupling of data and analysis of reports, maps, and graphs, allow decision makers to consider questions of customer
segmentation and targeting; market and site evaluation; business-to-business marketing; product distribution strategies;
and mergers, acquisitions and competitive analysis. For example, the DSS facilitates consideration of crucial, yet difficult
questions such as:
• Who are my best customers and where are they located?
• Which segments respond positively to my marketing campaign?
• How will the addition of a new site impact my existing locations?
• How can I analyze and define my market potential?
• How can I estimate demand for my products and services accurately?
• What impact will an acquisition have on my locations?
• How is the competition impacting my business?
Sauter, V.L. , Decision Support Systems for Business Intelligence, John Wiley, 2010
DSS in Action
Biologists working at UM-St. Louis and the Missouri Botanical Gardens
have used a specialized kind of DSS called a Geographic Information
System to tests hypotheses in phytogeographic studies. The GIS allows
for greater sophistication in studies of spatial components, such as the
movement patterns of fruit-eating birds. For example, the Loiselle Lab at
UM-St. Louis considered the Atlantic forests of Brazil and bird migration
using a GIS. They modeled the historic distributions of birds in this region
using a GIS and digitalized environmental layers from the National Atlas
of Brazil. These historic distributions were compared to the present
forest coverage to estimate the impact of the vast deforestation of this
area. This allowed Loiselle and her colleagues to estimates the original
habitat and the implications of its reduction. This, in turn, allowed them
to consider a wide range of options that impacted biodiversity
conservation decisions of these forests.
Sauter, V.L. , Decision Support Systems for Business Intelligence, John Wiley, 2010
Design Insights
Nobel laureate economist Herbert Simon points out:
"What information consumes is rather obvious: it
consumes the attention of its recipients. Hence a
wealth of information creates a poverty of attention,
and a need to allocate that attention efficiently
among the overabundance of information sources
that might consume it." (Scientific American, Sep 95,
p.201) Hence, as the amount of information
increases, so does the need for filtering processes
which help decision makers find that which is most
important and meaningful.
Sauter, V.L. , Decision Support Systems for Business Intelligence, John Wiley, 2010
DSS in Action
Today’s analytics provide more than just the profit level or sales quantity
of a store. With new data mining tools managers can now get insights
into why sales hit specific levels as well as what is likely to happen next
month, thus giving them factors that can be manipulated to improve
performance of shoppers. This, in turn, stimulates decisions such as how
to rearrange store layouts , stock shelves and price items. Once shopping
behaviors and preferences are understood, store then can tailor offerings
accordingly to differentiate themselves from competitors. Britain’s Tesco
relies on Mined data for most decisions, including the development of
house brands. Kroeger (U.S.) uses mined data to profile customer buying
behavior so they can better target coupons to make the store more
appealing. The ability to predict customer response to changes in
business rules provides a powerful competitive advantage for the store.
Sauter, V.L. , Decision Support Systems for Business Intelligence, John Wiley, 2010
DSS in Action
Jewish Hospital Healthcare Services uses various DSS applications in the areas of
productivity, cost accounting, case mix and nurse staff scheduling. The systems include
modeling, forecasting, planning, communications, database management systems and
graphics. Furthermore, all of the data are drawn from key clinical and financial systems
so there is not inconsistency in the data used by different decision makers. This allows
decision makers to consider problems and opportunities from more dimensions, with
better support than ever before. For example, the DSS includes a “nursing acuity
system” for classifying patients by the severity and nursing-needs associated with their
illnesses. These calculations can be used by the nurse staffing scheduling system to
estimate the demand for nurses on a daily basis. Not only does this system help nurse
managers to plan schedules, the DSS helps them to evaluate heuristics they might
employ in developing the schedule. For example, they can compare the estimated
nurse staffing needs to the actual levels to determine if there are better ways of
managing their staffs. In this era of managed care, such analyses help the hospitals use
scarce resources more effectively.
Sauter, V.L. , Decision Support Systems for Business Intelligence, John Wiley, 2010
DSS in Action
Hallmark, the 100 year old greeting card company, has used data
mining to improve the effectiveness of direct marketing
campaigns for its best customers. The company collects point-ofsales data, information about loyalty card holders and information
obtained from the customers themselves to understand how and
to what the customers respond. The analysis, which utilizes three
years of data at the UPC (product) level for individual customers,
provides profiles that help Hallmark understand what products to
market and at what time to market to individual customers.
Further, these analyses help Hallmark understand which of its
marketing campaigns are successful (or not) and where increased
marketing would bring additional revenues.
Sauter, V.L. , Decision Support Systems for Business Intelligence, John Wiley, 2010
DSS in Action
Data have begun to transform the management of professional sports. Managers who
intelligently use data and analytics can improve asset acquisition and management,
talent management, and operational performance. Billy Beane showed the world that
his ideas about using analytics could produce a low cost baseball team that was
competitive with those teams having a much higher payroll. Manager Billy, aided by
assistant Paul DePodesta, first with the aid of a decision support system (AVM systems)
and then on their own, broke down activities to predict a player’s ability to score runs
and used that knowledge to decide how to build and manage the lowest cost, winning
team in professional baseball. This effort was so amazing that when the Major League
Players Association created the Commissioner’s Blue Ribbon Panel on Baseball
Economics in 1999, they found Beane’s Oakland A’s to be an anomaly in their analysis. In
fact, it was sufficiently troubling that the Commission asked Mr. Beane to appear to
explain how he managed to be competitive. Some in baseball claimed he was just lucky.
However, Mr. Beane knows that it is to the effective use of analytics in his organization.
In fact, this use of analytical tools is chronicled in Michael Lewis’ best selling book,
Moneyball: The Art of Winning an Unfair Game.
Sauter, V.L. , Decision Support Systems for Business Intelligence, John Wiley, 2010
DSS in Action
The Obama Presidential campaign of 2008 used a DSS that they
called Neighbor to Neighbor. The campaign leveraged election
board data with data collected on websites, rallies, or through
telephone polls. The system included names and addresses of
voters whom they believed were undecided in the campaign. It
also included issues of interest to the specific voter, data about
issues of interest in a particular region, and past voting records.
Using this tool, staff members could more effectively identify
scripts and pitches to use with particular voters to convince them
to vote for Obama. In addition, they could customize fliers and
other campaign materials to get their point to the voters more
effectively. Near real-time data and sophisticated analytics
helped volunteers use valuable campaign time more effectively.
Sauter, V.L. , Decision Support Systems for Business Intelligence, John Wiley, 2010